Grading of Metacarpophalangeal Rheumatoid Arthritis on Ultrasound Images Using Machine Learning Algorithms
Grading of Metacarpophalangeal Rheumatoid Arthritis on Ultrasound Images Using Machine Learning Algorithms
复制标题
使用机器学习算法对超声图像上的掌指类风湿性关节炎进行分级
DOI：
10.1109/access.2020.2982027
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Wang, Fangfang
中科院分区：
文献类型：
--
作者：
Yang, Tengfei;Zhu, Haijiang;Wang, Fangfang
掌指关节类风湿性关节炎（RA）超声图像的分级评价是一个诊断挑战，在很大程度上依赖于训练有素的超声医师的专业知识。本研究提出了一种分级方法，用于检测和估计滑膜增厚和骨质侵蚀的几何和纹理特征。与以往的研究在这方面，这项工作使用的指标和纹理特征的区域感兴趣（ROI）。采用基于高斯尺度空间的分割方法，同时提取掌指骨的高亮特征和滑膜增厚的暗特征。对分割结果进行分析，提取3个量化的几何参数，结合灰度共生矩阵（GLCM）统计纹理特征描述掌指关节超声图像。为了获得较好的分类能力，我们采用了支持向量机（SVM）和各种特征描述符，包括灰度共生矩阵，局部二进制模式（LBP），和灰度共生矩阵+ LBP，掌指关节的超声图像分级。结果表明，支持向量机，基于我们的特征描述符，提供了高达92.50%的准确率，四个描述符。基于GLCM+LBP描述子的SVM对四个级别RA超声图像的判别准确率（86.55%）优于SVM + LBP（85.43%）或SVM+GLCM（82.51%）。总的来说，这种方法指出，掌指关节类风湿关节炎超声图像的显着分级没有医学专家分析或血液样本分析，如检测C-反应蛋白，测量红细胞沉降率，并测试类风湿因子。
The grading evaluation of metacarpophalangeal rheumatoid arthritis (RA) ultrasonic images is a diagnostic challenge that heavily relies on the expertise of trained sonographers. This study presents a grading method for detecting and estimating the geometric and texture features of synovium thickening and bone erosion. Unlike previous studies in this area, this work uses the metrics and texture features of region of interest (ROI). The highlighted feature of metacarpophalangeal bone and the dark feature of the synovial thickening are extracted simultaneously by the segmented method based on the Gaussian scale space. The segmented results are analyzed to extract three quantitative geometric parameters, which are combined with gray-level co-occurrence matrix (GLCM) statistic texture features to describe the ultrasonic image of metacarpophalangeal RA. To obtain the preferable ability of classification, we applied a support vector machine (SVM) and various feature descriptors, including GLCM, local binary patterns (LBP), and GLCM + LBP, to grade the ultrasonic image of metacarpophalangeal RA. Results show that the SVM, based on our feature descriptor, provides the highest accuracy of up to 92.50%, of the four descriptors. The SVM based on GLCM+LBP descriptor shows better accuracy (86.55%) than either SVM + LBP (85.43%) or SVM+GLCM (82.51%) for discriminating among four grade RA ultrasonic images. Overall, this methodology points to a significant grading of metacarpophalangeal RA ultrasound images without medical expert analysis or blood sample analysis, such as detecting C-reactive protein, measuring erythrocyte sedimentation rate, and testing rheumatoid factor.